Details

Autor(en) / Beteiligte

• Zhang, Chao
• Hu, Haojin
• Tai, Yonghang
• Yun, Lijun
• Zhang, Jun
• Meng, Weizhi
• Weizhi Meng

Titel

Trustworthy Image Fusion with Deep Learning for Wireless Applications

Ist Teil von

• Wireless communications and mobile computing, 2021, Vol.2021 (1)

Ort / Verlag

Oxford: Hindawi

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• To fuse infrared and visible images in wireless applications, the extraction and transmission of characteristic information security is an important task. The fused image quality depends on the effectiveness of feature extraction and the transmission of image pair characteristics. However, most fusion approaches based on deep learning do not make effective use of the features for image fusion, which results in missing semantic content in the fused image. In this paper, a novel trustworthy image fusion method is proposed to address these issues, which applies convolutional neural networks for feature extraction and blockchain technology to protect sensitive information. The new method can effectively reduce the loss of feature information by making the output of the feature extraction network in each convolutional layer to be fed to the next layer along with the production of the previous layer, and in order to ensure the similarity between the fused image and the original image, the original input image feature map is used as the input of the reconstruction network in the image reconstruction network. Compared to other methods, the experimental results show that our proposed method can achieve better quality and satisfy human perception.